The business model of the vast majority of cellular companies on Earth is built on the long term – regular monthly payments from customers. This model is also called LTV.
The risk of subscriber outflow for a cellular company is significant and can have serious consequences. When customers choose to switch to a different provider, the company loses not only their business but also potential future revenue from that customer. This can have a damaging impact on a company’s financial health, which is why retaining subscribers is crucial.
High levels of outflow may indicate that the service being provided isn’t meeting customers’ needs or that there are more competitive options available. To mitigate this risk, cellular companies typically invest in providing excellent customer support, offering attractive discounts and promotions, and continuously evolving their products and services to remain ahead of competitors.
Obviously, monitoring possible customer churn is an important task in this business. However, predicting when a customer will leave his provider and go to another one is not that easy. People usually do not inform cellular operators about their plans, just silently terminate the contract.
That’s where ML and PySpark come in handy for cellular companies, as they can use these technologies to predict which customers are likely to leave (aka “churn”) before it happens. By analyzing customer data like usage patterns and payment history, these models can identify potential churners and prompt companies to take action before losing valuable customers.
In this post I did quite a comprehensive research on the customer base of one cellular company. I have done research on customer behavior patterns, built summary tables, lots of visualizations and of course machine learning models. I hope you find this code useful and interesting. And maybe you will take something from it for your own use.